Predicting Seasonal Weather

January 12, 2010

Introduction

Large-scale weather patterns which occur in various locations around the Earth, from the El NiÃƒ±o-Southern Oscillation (ENSO) in the tropics to the high latitude Arctic Oscillation (AO) play a significant part in controlling the weather on a seasonal time scale. Knowing the condition of these atmospheric oscillations in advance would greatly improve long-range weather predictions. Scientists search for clues in the earth’s surface conditions such as tropical sea surface temperatures and snow cover at higher latitudes. Reliable and accurate weather prediction is vitally important in numerous areas of society, particularly agriculture and water management and weather risks are evaluated by a wide range of businesses, including power distributors who make fewer sales during cool summers and more sales during cold winters. The portion of the U.S. economy sensitive to weather conditions is estimated to be at least $3 trillion.

The winter of 2002-2003 offers one example of how large-scale patterns can impact a single season. It was supposed to be milder-than-average in the East, driven by the warmer than average sea surface temperatures over the central Pacific during 2002-03 (generated by a strong ENSO event), similar to other recent milder-than-average winters in the northern and eastern United States during other recent El NiÃƒ±o winters. Instead the biting cold of January propelled natural gas prices to an all-time high, and heavy snows paralyzed the transportation infrastructure in all the major eastern cities during February 2003 . The winter of 2003/04 offered little relief to many winter-weary cities in the East with a return to brutally cold temperatures in January. Why have forecasters and businesses been caught by surprise? One NSF funded scientist believes at least part of the answer lies in the frozen tundras of Siberia, where greater-than-average autumn snowfall causes weather patterns in the Arctic regions to shift southward into the midlatitudes during the winter, while less-than-average snowfall causes the patterns to retreat poleward.

A New And Better Way

Experimental real-time weather forecasts, which incorporate satellite observations of snow cover into a prediction model, have been more on-target in their winter forecasts than those of major government forecast centers in both the U.S. and Europe.

A National Science Foundation (NSF)-funded collaborative research effort between Atmospheric and Environmental Research, Inc. (AER Inc., www.aer.com ), and the Massachusetts Institute of Technology (MIT) (www.mit.edu) has led to a new understanding of the relationship between fall snow cover and winter climate variability.

Forecast centers often rely heavily on links between surface temperatures and precipitation patterns around the globe. However, the usefulness of this approach in seasonal climate prediction is limited by the frequency of significant El Nino events, which occur only once every 4-5 years. The predictive skill of El Nino-based temperature forecasts outside of the tropics often has been far off the mark.

El Nino means “Ëœthe boy’ or “ËœChrist Child’ in Spanish. The weather phenomenon called El Nino was so named for its tendency to arrive around Christmastime. Unusually warm water in the Pacific Ocean heralds the arrival of an El Nino.

El Nino is a disruption of Earth’s ocean-atmosphere system in the tropical Pacific Ocean, with consequences for weather around the globe. Increased rainfall across the southern tier of the U.S. and in Peru result in extreme flooding in these areas, and in drought in the western Pacific region, as well as widespread fires in Australia. In recent decades, El Nino has occurred in more rapid succession than in the past, a development possibly linked to global climate change. Recent El Ninos occurred during the winters of 1986-87, 1991-92, 1993, 1994, and 1997-98.

Researchers at AER and MIT are taking winter weather forecasting beyond El Nino by investigating the relationship between Siberian snow cover in fall months, and Northern Hemisphere climate variability during the winter. A forecast model developed by AER scientist Judah Cohen has consistently achieved on-target forecasts for most major cities in the industrialized countries.

“Weather impacts peoples’ lives and the global economy on a daily basis,” says Jay Fein, program director in NSF’s climate dynamics program. “Improving our ability to predict severe events such as the cold weather in the eastern U.S. this past winter, and the heavy snow during the prior winter, has obvious benefits. The success of Cohen’s real-time forecasts offers a way to improve our ability to anticipate important climate events.”

New Seasonal Forecast Model

The new seasonal forecast model has been continuously updated and tested using hindcasts and real-time forecasts. In addition to incorporating data on sea surface temperature, stratospheric conditions and snow cover extent, other inputs into the forecasts include anomalies from the fall season such as regional temperature and sea level pressure anomalies, and the propagation of energy associated with large atmospheric waves known as Rossby waves.

Model Accuracy Demonstrated

A striking example of the accuracy of the new method happened in the winter of 2002-03. Based on extensive Siberian snow cover during the fall, Cohen correctly forecasted cold weather in the eastern U.S., while most other forecasters predicted warm weather for the northern half of the U.S., based mainly on El NiÃƒ±o conditions. Similarly, Cohen’s forecast for the winter of 2003-04 indicated cold conditions in the northeastern U.S., while other forecasts anticipated an equal chance of warm, cold or normal weather. (see images 3, 4, forecasted temperature anomaly Jan-Feb-Mar 2004 and observed temperature anomaly Jan-Feb-Mar 2004, U.S. only)

Snow cover has a pronounced impact on large-scale waves in the atmosphere, so the advantage of factoring in snow cover to improve winter forecasts is not limited to the United States. Cohen issued a real-time winter forecast for Europe for 2003-04, which was significantly more accurate than those issued by the European forecast centers. The departure from normal temperature forecast for the entire Northern Hemisphere was compared to the observed departure from normal temperatures. The forecast showed an almost exact correspondence to observed temperatures across most of the land areas of the Northern Hemisphere with the exception of northwest Canada.

“Such an on-target forecast is highly improbable,” says Cohen, “With techniques that heavily depend on El Nino for information. Regions impacted by snow variability differ from those influenced by El Nino. The influence of snow cover extent has the potential to complement El Nino-derived forecasts, and to advance our understanding of climate variability and its application in prediction models.”

Observed Eurasian snow cover on October 1, 2003, is compared with observed Eurasian snow cover on November 1, 2003, as seen from NOAA satellites. Green represents land areas, dark blue is ocean, light blue is sea ice and Eurasian snow cover is colored in white.

October is the month where snow cover undergoes its greatest expansion in the Eurasian region. During October, Eurasian snow cover can increase by as much as 10-15 million square kilometers, which is greater than the total land area of the United States, including Alaska. NSF-funded research has shown that variability in the extent of Eurasian snow cover can be used to predict cold or warm winters across the entire mid-latitudes of the Northern Hemisphere.